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The purpose of this paper is to develop an understanding of how gay and lesbian spaces are constructed and deployed within the context of asylum claims by sexually diverse people. Through doing this, the author details the ways in which the present deployment of place, as a form of evidence for a relatively fixed conception of sexual difference, does not correspond to the self-conceptions of sexually diverse asylum seekers.

This article draws on the experiences of eight sexually diverse refugees who agreed to participate in semi-structured interviews. Deploying a queer narrative analysis approach, these experiences are explored to develop a detailed understanding of how sexually diverse spaces are constructed within refugee status determinations. This interview-led approach is combined with a critical epistemology informed by the queer theory to understand the role of place in the construction of sexual identity.

The central finding of this article is that engagement/attendance with/in particular places and spaces is overdetermined as a form of evidence of LGBTIQA+ identity within refugee status determination. Further findings relate to the relationship between places and sexual identities more generally. The paper helps to shed light on how sexually diverse identities are conceived in essentially ontological and fixed terms, with the result that places are often flattened, with the diversity and tensions within them being ignored and occluded.

The originality of this study emerges from the analysis of new qualitative data. This originality is strengthened by the successful combination of empirical research, queer theoretical insights and the application of this combination to policy. This remaining a relatively rare combination. In addition, in contrast to the existing literature, the paper looks specifically at how LGBTIQA+ or queer spaces are conceptualised within refugee status determination processes.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

The purpose of this paper is to reflect on the construct of phygital experiences and provide ideas that may spur future research on phygital consumer experiences in relation to consumer well-being using qualitative research methods.

With the increase in consumers’ online and offline interactions, there is a greater need for marketers to prompt integrated consumer experiences (i.e. integrated customer experiences through online and offline interactions). The authors developed this essay based on a literature review of phygital experiences and consumer well-being.

This commentary provides suggestions on how to expand the conceptual boundaries of phygital experiences by examining the effects of consumer phygital experiences in relation to consumer need satisfaction, consumer happiness and benefits to the firm. The commentary also includes several methodological suggestions that can guide future qualitative research.

The value of this commentary involves insights about research methods stimulated by the current research on consumer well-being.